Modeling Gross Primary Production in North America with MODIS Images and Reanalysis Climate Data

Abstract:

Accurate estimation of gross primary production (GPP) of terrestrial ecosystems is vital for a better understanding of the spatial-temporal patterns of the global carbon cycle. In this study we estimated GPP in North America (NA), using the satellite-based Vegetation Photosynthesis Model (VPM), MODIS images at 8-day temporal and 500 m spatial resolution, and NCEP-NARR reanalysis climate data. The simulated GPP (GPP_VPM) agreed well with the flux tower derived GPP (GPP_EC) at 39 AmeriFlux sites (155 site-years). The GPP_VPM in 2010 was spatially aggregated to 0.5 by 0.5 degree grid cell, and then evaluated with solar-induced chlorophyll fluorescence (SIF) data from GOME-2, which is often regarded as the proxy of direct measurement of vegetation photosynthesis. There were good agreements in spatial distribution and seasonal dynamics between GPP_VPM and SIF. At biome scale, the relationship between GPP_VPM and SIF showed strong linear correlations (R² > 0.95) and small variations of slopes (4.60 – 5.55 g C m^-2 year^-1 / (mW m^-2 nm^-1 sr^-1)). The total annual GPP_VPM in NA in 2010 was approximately 13.53 Pg C year^-1, which accounted for ~11.0% of the global terrestrial GPP and was within the range of annual GPP estimates from several other process-based and data-driven models (11.35 – 22.23 Pg C year^-1). Forests contributed most (4.17 Pg C year^-1) to the annual GPP in NA. Evergreen broadleaf forests were most productive with an average annual GPP exceeding 2000 g C m^-2 year^-1. The results from this study has demonstrated the reliable performance of VPM at the continental scale, and the resultant GPP product at 500-m spatial resolution provides more opportunities to improve the studies of carbon cycle, model inter-comparison, and benchmarking.